Article
Automation & Control Systems
Tangfan Xiahou, Zhiguo Zeng, Yu Liu
Summary: This article introduces a MoG-EHMM model that fuses expert knowledge and condition monitoring information for RUL prediction, demonstrating that the performance of RUL prediction can be substantially improved by incorporating expert knowledge with monitoring information.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Wenhan Zhang, Zhenhua Wang, Tarek Raissi, Rong Su
Summary: In this paper, a novel ellipsoid-based framework is proposed for fault estimation and remaining useful life prognosis. The framework consists of three steps: actuator fault interval estimation, parameter estimation using an ellipsoid-based extended Kalman filter, and prediction of future degradation states. Numerical simulations were conducted to verify the viability and validity of the approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Industrial
Chao Wang, Tao Zhu, Bing Yang, Minxuan Yin, Shoune Xiao, Guangwu Yang
Summary: A general RUL framework for predicting crack propagation of mechanical system structures based on limited condition monitoring data is proposed. For invisible crack states, a method considering the common characteristics of the object is proposed, using the crack propagation stage as the delay time and combining statistical distribution and hypothesis testing. For visible crack states, methods based on a hypothetical distribution and support vector regression with the Kalman filter, considering the current state and individual degradation characteristics, are proposed. The framework achieves a competitive effect in RUL of internal crack propagation structures of railway wagons, which has important theoretical and practical value for integrity assessment and reliability life prediction.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Thermodynamics
Lisen Yan, Jun Peng, Dianzhu Gao, Yue Wu, Yongjie Liu, Heng Li, Weirong Liu, Zhiwu Huang
Summary: This paper proposes a hybrid framework combining a model-based method and a data-driven method for accurately predicting the remaining useful life of lithium-ion batteries. The method improves prediction accuracy by dynamically updating parameters with particle filters and optimizing the performance of the support vector regression model using an artificial bee colony algorithm. Experimental results demonstrate the effectiveness of the proposed method, particularly in the early stage.
Article
Engineering, Mechanical
Brian Ellis, P. Stephan Heyns, Stephan Schmidt
Summary: This article introduces a hybrid approach for prognosis of mechanical components, which combines physics-based and data-driven methods and is applied to turbomachine rotor blades. Experimental results show that the hybrid approach outperforms other methods in predicting crack length and improves the accuracy and precision of remaining useful life estimation.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Shuai Zhao, Yingzhou Peng, Fei Yang, Enes Ugur, Bilal Akin, Huai Wang
Summary: Condition monitoring of power devices is crucial for safety and mission-critical systems. Noise and aperiodic degradation measurements can negatively impact health assessment performance, but a proposed method in this article addresses these challenges by incorporating uncertainties and using a stochastic expectation-maximization algorithm for parameter estimation. Numerical analysis and testing on SiC MOSFETs validate the accuracy and robustness of the method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Industrial
Xiaoyan Shao, Baoping Cai, Yonghong Liu, Junyan Zhang, Zhongfei Sui, Qiang Feng
Summary: A novel hybrid model-data-driven RUL prediction method based on a fusion of Kalman filter and dynamic Bayesian network is proposed in this paper. The method improves accuracy by enhancing the performance of observation values through DBN and considering estimation error and observation error. The uncertainty distribution of degradation parameters and environmental parameters is integrated into the state estimation model. Numerical simulation of a subsea Christmas tree valves demonstrates the advantages of the proposed RUL prediction method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Shuai Zhao, Shaowei Chen, Fei Yang, Enes Ugur, Bilal Akin, Huai Wang
Summary: By integrating potential failure precursors and optimizing them based on a degradation model, formulating a composite failure precursor can enhance the accurate prediction of remaining useful life in power electronic systems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Review
Energy & Fuels
Liyuan Shao, Yong Zhang, Xiujuan Zheng, Xin He, Yufeng Zheng, Zhiwei Liu
Summary: This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components, with a focus on lithium-ion batteries. The failure mechanism of energy storage components is clarified, and RUL prediction methods are summarized. The application of data-model fusion-based methods to RUL prediction of lithium-ion batteries is discussed, along with the challenges and future research outlook.
Article
Chemistry, Analytical
Jorgen F. Pedersen, Rune Schlanbusch, Thomas J. J. Meyer, Leo W. Caspers, Vignesh V. Shanbhag
Summary: This study investigates using acoustic emission (AE) sensors to identify early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF). The root mean square (RMS) feature is found to be a potent indicator for understanding leakage initiation.
Article
Engineering, Industrial
Zhenan Pang, Xiaosheng Si, Changhua Hu, Dangbo Du, Hong Pei
Summary: This article proposes a method for estimating the remaining useful life of degrading products by fusing accelerated degradation data and condition monitoring data. The method uses Bayesian inference and Markov Chain Monte Carlo (MCMC) to update posterior distributions of model parameters and approximate the RUL distribution, considering the randomness of model parameters. Validation using a practical case study of accelerometers shows that the method achieves higher RUL estimation accuracy and less uncertainty.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Multidisciplinary
David A. Najera-Flores, Zhen Hu, Mayank Chadha, Michael D. Todd
Summary: In order to predict the remaining useful life (RUL) of lithium-ion batteries, simplified physical laws and machine learning-based methods can be used to develop a capacity degradation model. While simplified physical models are easy to implement, they may result in large errors in failure prognostics. Data-driven models can provide more accurate degradation forecasting but may require a large amount of training data and may produce predictions inconsistent with physical laws. Existing methods also face challenges in predicting RUL at the early stages of battery life.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Artificial Intelligence
Anil Kumar, Chander Parkash, Hesheng Tang, Jiawei Xiang
Summary: The proposed intelligent framework seamlessly integrates degradation monitoring, defect identification, and remaining useful life (RUL) estimation for comprehensive and efficient bearing health assessment. The framework utilizes advanced techniques such as directed divergence measurement, graph convolution network, and dynamic analysis-assisted filtering to improve accuracy in anomaly detection and RUL estimation, ultimately enhancing system reliability and maintenance strategies.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Mechanical
Yang Chang, Jianxiao Zou, Shicai Fan, Chao Peng, Huajing Fang
Summary: This paper proposes a prognostic technique with the capability of uncertainty management, which consists of two phases to reduce the uncertainty and ensure the reliability of the prognostic result.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Jiri Sova, Petr Kolar, David Burian, Petr Vozabal
Summary: This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses real loading conditions and operation hours to calculate the spindle lifetime. The RUL is corrected using a bearing condition assessment based on vibration signal and measured data.
Article
Computer Science, Hardware & Architecture
Junbo Son, Yilu Zhang, Chaitanya Sankavaram, Shiyu Zhou
IEEE TRANSACTIONS ON RELIABILITY
(2015)
Article
Engineering, Industrial
Junbo Son, Patricia Flatley Brennan, Shiyu Zhou
Article
Engineering, Industrial
Raed Kontar, Junbo Son, Shiyu Zhou, Chaitanya Sankavaram, Yilu Zhang, Xinyu Du
Article
Mathematical & Computational Biology
Junbo Son, Patricia Flatley Brennan, Shiyu Zhou
STATISTICS IN MEDICINE
(2017)
Article
Statistics & Probability
Raed Kontar, Shiyu Zhou, Chaitanya Sankavaram, Xinyu Du, Yilu Zhang
Article
Computer Science, Information Systems
Junbo Son, Patricia Flatley Brennan, Shiyu Zhou
Article
Management
Guanyi Lu, Hyun Seok (Huck) Lee, Junbo Son
Summary: By analyzing the impact of product variety on sales at the stock-keeping unit (SKU) level, this study introduces two dimensions of product variety and finds that increasing variety only increases sales of SKUs belonging to the brand whose variety has increased, while it decreases sales of other SKUs. The effects of product variety vary significantly across different profitability levels and product categories, with the strongest positive effect observed on high-margin products.
JOURNAL OF OPERATIONS MANAGEMENT
(2022)
Article
Information Science & Library Science
Junbo Son, Yeongin Kim, Shiyu Zhou
Summary: This paper explores how patients' trust in the health information system in the IoT era affects their adherence to system recommendations, aiming to improve asthma management by designing optimal alerting strategies. The study concludes that patient trust can change over time based on experience, influencing future adherence behavior. The approach significantly enhances patients' quality of life and provides valuable insights for patients, healthcare practitioners, and technology-enabled healthcare companies.
INFORMATION TECHNOLOGY & MANAGEMENT
(2022)
Article
Engineering, Industrial
Xiaomeng Peng, Xiaoning Jin, Shiming Duan, Chaitanya Sankavaram
Summary: This research presents a robust and cost-effective FDD framework that integrates active learning and semi-supervised learning methods to detect both known and unknown failure modes. The framework strategically selects informative samples to be annotated from fully unlabeled data, minimizing labeling cost, and demonstrates superior performance in experiments.
Article
Engineering, Industrial
Yuanyuan Gao, Xinming Wang, Junbo Son, Xiaowei Yue, Jianguo Wu
Summary: In this article, a modeling approach is proposed for predicting porosity and reconstructing microstructure in additive manufacturing. The Two-Point Correlation Function is used to quantitatively capture the morphology of pores and establish their relationship with process parameters. The results demonstrate the effectiveness and advantageous features of this method in both simulation studies and real-world applications.
Article
Automation & Control Systems
Jaesung Lee, Junbo Son, Shiyu Zhou, Yong Chen
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2020)
Proceedings Paper
Health Care Sciences & Services
Azeem Sarwar, Chaitanya Sankavaram, Xiangxing Lu
2017 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM)
(2017)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)